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Related Questions
- What are the key techniques used in word embeddings to capture semantic relationships between words?
- How do word embeddings, such as Word2Vec and GloVe, represent word meanings in a vector space?
- Can you explain the concept of semantic shift and how it affects word embeddings?
- How do word embeddings impact the accuracy of topic modeling techniques, such as Latent Dirichlet Allocation (LDA)?
- What role do word embeddings play in improving the coherence and interpretability of topic models?
- How do word embeddings handle out-of-vocabulary words and rare words in topic modeling?
- Can you discuss the relationship between word embeddings and named entity recognition (NER) in topic modeling?
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